ArticleTranslational lung cancer research2021
Conventional and semi-automatic histopathological analysis of tumor cell content for multigene sequencing of lung adenocarcinoma.
Article in Translational lung cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 15 citations in OpenAlex.
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- A universal platform for simultaneous TCRα/β removal enables safer and more potent TCR therapies and autoimmune modeling.bioRxiv : the preprint server for biology · 2026Article
- Tumor Cell Proportion Assessment in Advanced Non-Squamous Non-Small Cell Lung Cancer Tissue Samples in Real-World Settings in Japan: The ASTRAL Study.Diagnostics (Basel, Switzerland) · 2025Article
- The routine use of a digital tool for the tumor cell fraction quantification in molecular pathology: an international validation of QuANTUM.Pathologica · 2025Article
- Digital counting of tissue cells for molecular analysis: the QuANTUM pipeline.Virchows Archiv : an international journal of pathology · 2025Article
- Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis.The Journal of pathology · 2025Article
- Concordance in the estimation of tumor percentage in non-small cell lung cancer using digital pathology.Scientific reports · 2024Article
- Current challenges and practical aspects of molecular pathology for non-small cell lung cancers.Virchows Archiv : an international journal of pathology · 2024Review
- The Swin-Transformer network based on focal loss is used to identify images of pathological subtypes of lung adenocarcinoma with high similarity and class imbalance.Journal of cancer research and clinical oncology · 2023Article
- Quantitative Evaluation of Inflammatory Markers in Peri-Implantitis and Periodontitis Tissues: Digital vs. Manual Analysis-A Proof of Concept Study.Medicina (Kaunas, Lithuania) · 2022Article
- Biomarkers of response to checkpoint inhibitors beyond PD-L1 in lung cancer.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Review
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32 authors at 8 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundTargeted genetic profiling of tissue samples is paramount to detect druggable genetic aberrations in patients with non-squamous non-small cell lung cancer (NSCLC). Accurate upfront estimation of tumor cell content (TCC) is a crucial pre-analytical step for reliable testing and to avoid false-negative results. As of now, TCC is usually estimated on hematoxylin-eosin (H&E) stained tissue sections by a pathologist, a methodology that may be prone to substantial intra- and interobserver variability. Here we the investigate suitability of digital pathology for TCC estimation in a clinical setting by evaluating the concordance between semi-automatic and conventional TCC quantification.
methodsTCC was analyzed in 120 H&E and thyroid transcription factor 1 (TTF-1) stained high-resolution images by 19 participants with different levels of pathological expertise as well as by applying two semi-automatic digital pathology image analysis tools (HALO and QuPath).
resultsAgreement of TCC estimations [intra-class correlation coefficients (ICC)] between the two software tools (H&E: 0.87; TTF-1: 0.93) was higher compared to that between conventional observers (0.48; 0.47). Digital TCC estimations were in good agreement with the average of human TCC estimations (0.78; 0.96). Conventional TCC estimators tended to overestimate TCC, especially in H&E stainings, in tumors with solid patterns and in tumors with an actual TCC close to 50%.
conclusionsOur results determine factors that influence TCC estimation. Computer-assisted analysis can improve the accuracy of TCC estimates prior to molecular diagnostic workflows. In addition, we provide a free web application to support self-training and quality improvement initiatives at other institutions.
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